Supervised texture classification using a novel compression-based similarity measure

Authors:
Mehrdad J. Gangeh;Ali Ghodsi;Mohamed S. Kamel
Affiliations:
Center for Pattern Analysis and Machine Intelligence, Department of Electrical and Computer Engineering, University of Waterloo, Ontario, Canada;Department of Statistics and Actuarial Science, University of Waterloo, Ontario, Canada;Center for Pattern Analysis and Machine Intelligence, Department of Electrical and Computer Engineering, University of Waterloo, Ontario, Canada
Venue:
ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
Year:
2012

Citing 10
Cited 0

Filtering for Texture Classification: A Comparative Study

IEEE Transactions on Pattern Analysis and Machine Intelligence
Standard Codecs: Image Compression to Advanced Video Coding

Standard Codecs: Image Compression to Advanced Video Coding
Supervised texture classification by integration of multiple texture methods and evaluation windows

Image and Vision Computing
Dictionary based color image retrieval

Journal of Visual Communication and Image Representation
Image description using joint distribution of filter bank responses

Pattern Recognition Letters
Handbook of Texture Analysis

Handbook of Texture Analysis
Multi-level pixel-based texture classification through efficient prototype selection via normalized cut

Pattern Recognition
A compression‐based distance measure for texture

Statistical Analysis and Data Mining
The similarity metric

IEEE Transactions on Information Theory
Clustering by compression

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

Supervised pixel-based texture classification is usually performed in the feature space. We propose to perform this task in (dis)simil-arity space by introducing a new compression-based (dis)similarity measure. The proposed measure utilizes two dimensional MPEG-1 encoder, which takes into consideration the spatial locality and connectivity of pixels in the images. The proposed formulation has been carefully designed based on MPEG encoder functionality. To this end, by design, it solely uses P-frame coding to find the (dis)similarity among patches/images. We show that the proposed measure works properly on both small and large patch sizes. Experimental results show that the proposed approach significantly improves the performance of supervised pixel-based texture classification on Brodatz and outdoor images compared to other compression-based dissimilarity measures as well as approaches performed in feature space. It also improves the computation speed by about 40% compared to its rivals.